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AI at Microsoft

Editor's Notes

  1. Supervised: All data is labeled and the algorithms learn to predict the output from the input data. Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”. Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.